Immunological network signatures of cancer progression and survival

Tumor microenvironment: barrier or opportunity towards effective cancer therapy

Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME’s individual components and their interplay at higher resolution. Deeper understanding of the immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME.

A Nomogram Based on Circulating CD4+ T Lymphocytes and Lactate Dehydrogenase to Predict Distant Metastasis in Patients with Nasopharyngeal Carcinoma

Purpose

Distant metastasis is the main pattern of treatment failure in nasopharyngeal carcinoma (NPC) in the era of intensity-modulated radiotherapy (IMRT). We aimed to establish and validate a prognostic nomogram to identify patients with a high risk of distant metastasis.

Patients and Methods

A total of 503 patients with nonmetastatic NPC were included in this retrospective study. We established a prognostic nomogram for distant metastasis-free survival (DMFS) based on the Cox proportional hazards model. The predictive discriminative ability and accuracy of the nomogram were assessed with the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve. The nomogram’s clinical utility was also evaluated using decision curve analysis (DCA) and Kaplan–Meier method. The predictive ability of the nomogram was validated in an independent cohort.

Results

The multivariate analysis showed that circulating CD4⁺ T lymphocytes, lactate dehydrogenase (LDH), serum ferritin (SF), and N stage were independent prognostic factors for DMFS. Then, we constructed the nomogram based on these factors. The C-indexes of the nomogram for distant metastasis were 0.763 (95% CI: 0.685–0.841) and 0.760 (95% CI: 0.643–0.877) in the training cohort and validation cohort, respectively, which was higher than the 8th TNM staging system (0.672 and 0.677). The calibration curve showed that the prediction results of the nomogram were in high agreement with the actual observation. The ROC curve indicated that the nomogram had a better predictive ability than TNM staging. The DCA also demonstrated that the nomogram was clinically beneficial. In addition, the patients were classified into two different risk groups (high-risk, low-risk) by the nomogram.

Conclusion

As a supplement to TNM staging, our nomogram could provide a more effective and accurate prognostic prediction of distant metastasis in NPC patients. It has the potential to guide the individualized treatment of patients to improve their survival.

Profiling networks of distinct immune-cells in tumors

Background

It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in complex tissue, at higher-resolution and at various stages of differentiation, activation or function. This is particularly so for closely related immune-cells with diminutive, yet critical, differences.

Results

To predict networks of infiltrated distinct immune-cell phenotypes at higher resolution, we explored an integrated knowledge-based approach to select immune-cell signature genes integrating not only expression enrichment across immune-cells, but also an automatic capture of relevant immune-cell signature genes from the literature. This knowledge-based approach was integrated with resources of immune-cell specific protein networks, to define signature genes of distinct immune-cell phenotypes. We demonstrate the utility of this approach by profiling signatures of distinct immune-cells, and networks of immune-cells, from metastatic melanoma patients who had undergone chemotherapy. The resultant bioinformatics strategy complements immunohistochemistry from these tumors, and predicts both tumor-killing and immunosuppressive networks of distinct immune-cells in responders and non-responders, respectively. The approach is also shown to capture differences in the immune-cell networks of BRAF versus NRAS mutated metastatic melanomas, and the dynamic changes in resistance to targeted kinase inhibitors in MAPK signalling.

Conclusions

This integrative bioinformatics approach demonstrates that capturing the protein network signatures and ratios of distinct immune-cell in the tumor microenvironment maybe an important factor in predicting response to therapy. This may serve as a computational strategy to define network signatures of distinct immune-cells to guide immuno-pathological discovery.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1141-3) contains supplementary material, which is available to authorized users.

Evaluation of serum osteopontin level and gene polymorphism as biomarkers: analyses from the Nordic Adjuvant Interferon alpha Melanoma trial

Malignant melanoma is highly aggressive cancer with poor prognosis and few therapeutic options. Interferon alpha (IFN-α) has been tested as adjuvant immunotherapy in high-risk melanoma patients in a number of studies, but its beneficial role is controversial. Although IFN-α treatment can prolong relapse-free survival, the effect on overall survival is not significant. However, a small subset of patients benefits from the treatment, signifying the need for biomarkers able to identify a responding subgroup. Here we evaluated whether serum osteopontin (OPN) could function as a biomarker identifying patients with poor prognosis that might benefit from IFN-α. The choice of osteopontin was based on the knowledge about the dual role of this protein in cancer and immune response, an apparent association between OPN and IFN signaling and a prognostic value of OPN in multiple other tumor types. Serum samples from 275 high-risk melanoma patients enrolled in the Nordic Adjuvant IFN Melanoma trial were analyzed for circulating OPN concentrations and OPN promoter polymorphisms in position -443. The potential relation between serum OPN levels, the genotypes and survival in non-treated patients and patients receiving adjuvant IFN-α was investigated. Although slightly better survival was observed in the treated patients that had high levels of OPN, the difference was not statistically significant. In conclusion, serum OPN (its level or the genotype) cannot distinguish melanoma patients with poor prognosis, or patients that might benefit from adjuvant treatment with IFN-α.

Overcoming tumor immune evasion with an unique arbovirus

Combining dendritic cell vaccination with the adjuvant effect of a strain of dengue virus may be a way to overcome known tumor immune evasion mechanisms. Dengue is unique among viruses as primary infections carry lower mortality than the common cold, but secondary infections carry significant risk of hypovolemic shock. While current immuno-therapies rely on a single axis of attack, this approach combines physiological (hyperthermic reduction of tumor perfusion), immunological (activation of effector cells of the adaptive and innate immune system), and apoptosis-inducing pathways (sTRAIL) to destroy tumor cells. The premise of using multiple mechanisms of action in synergy with a decline in the ability of the tumor cells to employ resistance methods suggests the potential of this combination approach in cancer immunotherapy.

Differential protein network analysis of the immune cell lineage

Recently, the Immunological Genome Project (ImmGen) completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks.

Protein Quantification by MRM for Biomarker Validation

In this chapter we describe how mass spectrometry-based quantitative protein measurements by multiple reaction monitoring (MRM) have opened up the opportunity for the assembly of large panels of candidate protein biomarkers that can be simultaneously validated in large clinical cohorts to identify diagnostic protein biomarker signatures. We outline a workflow in which candidate protein biomarker panels are initially assembled from multiple diverse sources of discovery data, including proteomics and transcriptomics experiments, as well as from candidates found in the literature. Subsequently, the individual candidates in these large panels may be prioritised by application of a range of bioinformatics tools to generate a refined panel for which MRM assays may be developed. We describe a process for MRM assay design and implementation, and illustrate how the data generated from these multiplexed MRM measurements of prioritised candidates may be subjected to a range of statistical tools to create robust biomarker signatures for further clinical validation in large patient sample cohorts. Through this overall approach MRM has the potential to not only support individual biomarker validation but also facilitate the development of clinically useful protein biomarker signatures.

Quantifying the complexity of medical research

MOTIVATION

A crucial phenomenon of our times is the diminishing marginal returns of investments in pharmaceutical research and development. A potential reason is that research into diseases is becoming increasingly complex, and thus more burdensome, for humans to handle. We sought to investigate whether we could measure research complexity by analyzing the published literature.

RESULTS

Through the text mining of the publication record of multiple diseases, we have found that the complexity and novelty of disease research has been increasing over the years. Surprisingly, we have also found that research on diseases with higher publication rate does not possess greater complexity or novelty than that on less-studied diseases. We have also shown that the research produced about a disease can be seen as a differentiated area of knowledge within the wider biomedical research. For our analysis, we have conceptualized disease research as a parallel multi-agent search in which each scientific agent (a scientist) follows a search path based on a model of a disease. We have looked at trends in facts published for diseases, measured their diversity and turnover using the entropy measure and found similar patterns across disease areas.

CONTACT

raul.rodriguez-esteban@roche.com.

The Functional Genomics Network in the evolution of biological text mining over the past decade

Different programs of The European Science Foundation (ESF) have contributed significantly to connect researchers in Europe and beyond through several initiatives. This support was particularly relevant for the development of the areas related with extracting information from papers (text-mining) because it supported the field in its early phases long before it was recognized by the community. We review the historical development of text mining research and how it was introduced in bioinformatics. Specific applications in (functional) genomics are described like it's integration in genome annotation pipelines and the support to the analysis of high-throughput genomics experimental data, and we highlight the activities of evaluation of methods and benchmarking for which the ESF programme support was instrumental.

CLC and IFNAR1 are differentially expressed and a global immunity score is distinct between early- and late-onset colorectal cancer

Colorectal cancer (CRC) incidence increases with age, and early onset of the disease is an indication of genetic predisposition, estimated to cause up to 30% of all cases. To identify genes associated with early-onset CRC, we investigated gene expression levels within a series of young patients with CRCs who are not known to carry any hereditary syndromes (n=24; mean 43 years at diagnosis), and compared this with a series of CRCs from patients diagnosed at an older age (n=17; mean 79 years). Two individual genes were found to be differentially expressed between the two groups, with statistical significance; CLC was higher and IFNAR1 was less expressed in early-onset CRCs. Furthermore, genes located at chromosome band 19q13 were found to be enriched significantly among the genes with higher expression in the early-onset samples, including CLC. An elevated immune content within the early-onset group was observed from the differentially expressed genes. By application of outlier statistics, H3F3A was identified as a top candidate gene for a subset of the early-onset CRCs. In conclusion, CLC and IFNAR1 were identified to be overall differentially expressed between early- and late-onset CRC, and are important in the development of early-onset CRC.